Source Of Summary:

Lec.1.D. M. Ahlia spring 2024.pdf

Data Mining – Lecture 1

1. Why Data Mining? 🔍💾

• Explosive Growth of Data:
  • Data is growing from terabytes to petabytes! 📈
• Data Collection & Availability:
  • Automated data collection tools, advanced database systems, the Web, and our computerized society all contribute. 🌐🤖
• Major Sources of Abundant Data:
  • Business: Web activity, e-commerce, transactions, stocks, etc. 💼💳
  • Science: Remote sensing, bioinformatics, scientific simulations, etc. 🔬🧬
  • Society & Individuals: News, digital cameras, and more. 📸📰

Remember:

"We are drowning in data, but starving for knowledge!"

"Necessity is the mother of invention."

Data mining is the automated analysis of massive data sets to extract valuable insights.

2. What is Data Mining? 🤔

• Definition: Data mining is the search for relationships and global patterns that exist in large databases but are hidden among the vast amount of data. These relationships represent valuable knowledge about the database and its objects.
• Alternative Name:
  • Also known as Knowledge Discovery in Databases (KDD).

3. The Knowledge Discovery Process 🧩💡

Data mining is the core of the entire knowledge discovery process. Here’s how it unfolds:

1. Data Cleaning:
   • Remove noise and inconsistent data. 🧹
2. Data Integration:
   • Combine data from multiple sources. 🔗
3. Data Selection:
   • Retrieve the data relevant for analysis. 🎯